Tootfinder

Germany faces constraints in supplying more patriot air-defense systems to Ukraine, says Defense Minister: https://benborges.xyz/2025/07/14/germany-faces-constraints-in-supplying.html

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
https://arxiv.org/abs/2510.11393

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems' operational space, with hard constraints ensuring safety-critical requirements and soft constraints encoding performance or task objectives. Initially, all constraints are consolidated into two separate scalar time-varying hard and soft constraint fu…

Solving Set Constraints with Comprehensions and Bounded Quantifiers
Mudathir Mohamed, Nick Feng, Andrew Reynolds, Cesare Tinelli, Clark Barrett, Marsha Chechik
https://arxiv.org/abs/2508.08496

Solving Set Constraints with Comprehensions and Bounded Quantifiers
Many real applications problems can be encoded easily as quantified formulas in SMT. However, this simplicity comes at the cost of difficulty during solving by SMT solvers. Different strategies and quantifier instantiation techniques have been developed to tackle this. However, SMT solvers still struggle with quantified formulas generated by some applications. In this paper, we discuss the use of set-bounded quantifiers, quantifiers whose variable ranges over a finite set. These quantifiers can…

Low-redshift constraints on structure growth from CMB lensing tomography
Andrea Rubiola, Matteo Zennaro, Carlos Garc\'ia-Garc\'ia, David Alonso
https://arxiv.org/abs/2510.09563

Low-redshift constraints on structure growth from CMB lensing tomography
We present constraints on the amplitude of matter fluctuations from the clustering of galaxies and their cross-correlation with the gravitational lensing convergence of the cosmic microwave background (CMB), focusing on low redshifts ($z\lesssim0.3$), where potential deviations from a perfect cosmological constant dominating the growth of structure could be more prominent. Specifically, we make use of data from the 2MASS photometric survey (\tmpz) and the \wisc galaxy survey, in combination wit…

Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions
Sergey Petrov, Samson Lasaulce, Merouane Debbah
https://arxiv.org/abs/2508.09646

Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions
Precoding matrix construction is a key element of the wireless signal processing using the multiple-input and multiple-output model. It is established that the problem of global throughput optimization under per-antenna power constraints belongs, in general, to the class of monotonic optimization problems, and is unsolvable in real-time. The most widely used real-time baseline is the suboptimal solution of Zero-Forcing, which achieves a cubic complexity by discarding the background noise coeffi…

Remoteness, order, size and connectivity constraints in digraphs
Sufiyan Mallu
https://arxiv.org/abs/2510.08841 https://arxiv.org/pdf/2510.08841

Remoteness, order, size and connectivity constraints in digraphs
Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( ρ(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remoteness of a strong digraph with given order, size, and vertex-connectivity. We then characterise the extremal digraphs that maximise remote…

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

Calibrating Generative Models
Henry D. Smith, Nathaniel L. Diamant, Brian L. Trippe
https://arxiv.org/abs/2510.10020 https://arxiv.org/pdf/2510.10020

Calibrating Generative Models
Generative models frequently suffer miscalibration, wherein class probabilities and other statistics of the sampling distribution deviate from desired values. We frame calibration as a constrained optimization problem and seek the closest model in Kullback-Leibler divergence satisfying calibration constraints. To address the intractability of imposing these constraints exactly, we introduce two surrogate objectives for fine-tuning: (1) the relax loss, which replaces the constraint with a miscal…

Observational Constraints on the Radial Evolution of O$^{6 }$ Temperature and Differential Flow in the Inner Heliosphere
Yeimy J. Rivera, Kristopher G. Klein, Joseph H. Wang, Lorenzo Matteini, Daniel Verscharen, Jesse T. Coburn, Samuel T. Badman, Susan T. Lepri, Ryan M. Dewey, Jim M. Raines, Benjamin L. Alterman, Timothy J. Stubbs, Kevin C. Delano, Roberto Livi, Stefano A. Livi, Antoinette B. Galvin, Christopher J. Owen, Michael L. Stevens

Observational Constraints on the Radial Evolution of O$^{6+}$ Temperature and Differential Flow in the Inner Heliosphere
Over decades of solar wind observations, heavy ions have been observed to have a higher temperature and flow faster than protons in the solar corona and heliosphere. Remote observations have largely been limited to the low corona ($< 4R_{\odot}$), while in situ observations for heavy ions ($Z>2$) have only been sampled at 1 au and beyond. As a result, theories that address heavy ion heating and acceleration remain largely unconstrained. With the launch of Solar Orbiter, heavy ion kinetics can b…

Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints
Huanyu Yan, Yu Huo, Min Lu, Weitong Ou, Xingyan Shi, Ruihe Shi, Xiaoying Tang
https://arxiv.org/abs/2508.08772

Optimal Boost Design for Auto-bidding Mechanism with Publisher Quality Constraints
Online bidding is crucial in mobile ecosystems, enabling real-time ad allocation across billions of devices to optimize performance and user experience. Improving ad allocation efficiency is a long-standing research problem, as it directly enhances the economic outcomes for all participants in advertising platforms. This paper investigates the design of optimal boost factors in online bidding while incorporating quality value (the impact of displayed ads on publishers' long-term benefits). To a…

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
Yuanchun Guo, Bingyan Liu, Yulong Sha, Zhensheng Xian
https://arxiv.org/abs/2509.08750

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
Federating heterogeneous models on edge devices with diverse resource constraints has been a notable trend in recent years. Compared to traditional federated learning (FL) that assumes an identical model architecture to cooperate, model-heterogeneous FL is more practical and flexible since the model can be customized to satisfy the deployment requirement. Unfortunately, no prior work ever dives into the existing model-heterogeneous FL algorithms under the practical edge device constraints and p…

Online Safety under Multiple Constraints and Input Bounds using gatekeeper: Theory and Applications
Devansh R. Agrawal, Dimitra Panagou
https://arxiv.org/abs/2508.09963 https://…

Online Safety under Multiple Constraints and Input Bounds using gatekeeper: Theory and Applications
This letter presents an approach to guarantee online safety of a cyber-physical system under multiple state and input constraints. Our proposed framework, called gatekeeper, recursively guarantees the existence of an infinite-horizon trajectory that satisfies all constraints and system dynamics. Such trajectory is constructed using a backup controller, which we define formally in this paper. gatekeeper relies on a small number of verifiable assumptions, and is computationally efficient since it…

In-situ Value-aligned Human-Robot Interactions with Physical Constraints
Hongtao Li, Ziyuan Jiao, Xiaofeng Liu, Hangxin Liu, Zilong Zheng
https://arxiv.org/abs/2508.07606 https:…

In-situ Value-aligned Human-Robot Interactions with Physical Constraints
Equipped with Large Language Models (LLMs), human-centered robots are now capable of performing a wide range of tasks that were previously deemed challenging or unattainable. However, merely completing tasks is insufficient for cognitive robots, who should learn and apply human preferences to future scenarios. In this work, we propose a framework that combines human preferences with physical constraints, requiring robots to complete tasks while considering both. Firstly, we developed a benchmar…

A control theoretical approach to gene regulation raises quantitative constraints for dynamic homeostasis in stochastic gene expression
Guilherme Giovanini, Cyro von Zuben de Valega Negr\~ao, Ammar Alsinai, Alexandre Ferreira Ramos
https://arxiv.org/abs/2508.09038

A control theoretical approach to gene regulation raises quantitative constraints for dynamic homeostasis in stochastic gene expression
Cell phenotype dynamic homeostasis contrasts with the inherent randomness of intracellular reactions. Although feedback control of master regulatory genes (MRG) is a key strategy for maintaining gene network expression ranges limited, understanding the quantitative constraints and corresponding mechanisms enabling such a dynamic stability under noise remains elusive. Here we model MRG expression as a stochastic process and downstream genes as sensors which response conditionally induce MRG acti…

Nash Equilibria of Noncooperative/Miexd Differential Games with Density Constraints in Infinite Dimensions
Zhun Gou, Nan-Jing Huang, Jian-Hao Kang, Jen-Chih Yao
https://arxiv.org/abs/2508.07623

Nash Equilibria of Noncooperative/Miexd Differential Games with Density Constraints in Infinite Dimensions
Motivated by Cournot models, this paper proposes novel models of the noncooperative and cooperative differential games with density constraints in infinite dimensions, where markets consist of infinite firms and demand dynamics are governed by controlled differential equations. Markets engage in noncooperative competition with each other, while firms within each market engage in noncooperative or cooperative games. The main problems are to find the noncooperative Nash equilibrium (NNE) of the n…

Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
David Mguni, Yaqi Sun, Haojun Chen, Amir Darabi, Larry Olanrewaju Orimoloye, Yaodong Yang
https://arxiv.org/abs/2508.08800

Fault Tolerant Multi-Agent Learning with Adversarial Budget Constraints
In multi-agent systems, the safe and reliable execution of tasks often depends on agents correctly coordinating their actions. However, in real-world deployments, failures of computational components are inevitable, presenting a critical challenge: ensuring that multi-agent reinforcement learning (MARL) policies remain effective even when some agents malfunction. We propose the Multi-Agent Robust Training Algorithm (MARTA), a plug-and-play framework for training MARL agents to be resilient to p…

I had missed that last month, the condo development in the south parking lot of Fairview Mall was scaled back (at least for the first phase)
It's now just 1 tower instead of 3, and it's a rental instead of condos.
It's a really bad market to sell condos right now, so it's not too surprising.

Constraints on Ultra-heavy DM from TeV-PeV gamma-ray diffuse measurements
Manuel Rocamora, Pedro De La Torre Luque, Miguel A. S\'anchez-Conde
https://arxiv.org/abs/2509.09609

Constraints on Ultra-heavy DM from TeV-PeV gamma-ray diffuse measurements
Recent experiments have measured the Galactic $γ$-ray diffuse emission up to PeV energies, opening a window to study acceleration of Galactic cosmic rays and their propagation up to the cosmic-ray knee. Furthermore, these observations provide a powerful tool to set strong constraints into very-heavy dark matter particles, with masses in the TeV-PeV range. In this paper, we explore the potential of the newest observations of diffuse emissions at the Galactic plane from HAWC and LHAASO to probe …

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
Luis Rojas, Sebasti\'an Espinoza, Esteban Gonz\'alez, Carlos Maldonado, Fei Luo
https://arxiv.org/abs/2510.09876

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied t…

R\'enyi Law Constraints on Gau{\ss}-Bonnet Black Hole Merger
Neeraj Kumar, Ankur Srivastav, Phongpichit Channuie
https://arxiv.org/abs/2509.08362 https://

Rényi Law Constraints on Gauß-Bonnet Black Hole Merger
In this article, we explore the Rényi law constraints on black hole merger in Gauß-Bonnet (GB) gravity. Specifically, we consider the case of static solutions in five-dimensional (5D) Anti-de-Sitter (AdS) spacetime and study the constraints on merger of two equal mass black holes. We calculate the general Rényi entropy expression and utilize it to study the bounds on the final black hole mass post-merger. We study its variation with the Rényi parameter. We also compare the results with thos…

TEN: Table Explicitization, Neurosymbolically
Nikita Mehrotra, Aayush Kumar, Sumit Gulwani, Arjun Radhakrishna, Ashish Tiwari
https://arxiv.org/abs/2508.09324 https://

TEN: Table Explicitization, Neurosymbolically
We present a neurosymbolic approach, TEN, for extracting tabular data from semistructured input text. This task is particularly challenging for text input that does not use special delimiters consistently to separate columns and rows. Purely neural approaches perform poorly due to hallucinations and their inability to enforce hard constraints. TEN uses Structural Decomposition prompting - a specialized chain-of-thought prompting approach - on a large language model (LLM) to generate an initial …

Incoherent Imaging with Spatially Structured Quantum Probes
Anthony J. Brady, Zihao Gong, Alexey V. Gorshkov, Saikat Guha
https://arxiv.org/abs/2510.09521 https://

Incoherent Imaging with Spatially Structured Quantum Probes
Incoherent imaging, including fluorescence and absorption microscopy, is often limited by weak signals and resolution constraints -- notoriously, Rayleigh's curse. We investigate how spatially structured quantum probes, combined with quantum detection strategies like spatial mode demultiplexing and photon counting, overcome these limitations. We propose a novel imaging protocol based on twin-beam echoes that maps the generalized incoherent-imaging model -- comprising both absorption and fluores…

Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Ori Livson, Mikhail Prokopenko
https://arxiv.org/abs/2510.09076 https://ar…

Arrow's Impossibility Theorem as a Generalisation of Condorcet's Paradox
Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The theorem is often described as a generalisation of Condorcet's Paradox, wherein pairwise majority voting may fail to jointly satisfy the same constraints due to the occurrence of elections that result in contradictory preference cycles. However, a formal proof of t…

Age of Information Minimization in Goal-Oriented Communication with Processing and Cost of Actuation Error Constraints
Rishabh S. Pomaje, Jayanth S., Rajshekhar V. Bhat, Nikolaos Pappas
https://arxiv.org/abs/2508.07865

Age of Information Minimization in Goal-Oriented Communication with Processing and Cost of Actuation Error Constraints
We study a goal-oriented communication system in which a source monitors an environment that evolves as a discrete-time, two-state Markov chain. At each time slot, a controller decides whether to sample the environment and if so whether to transmit a raw or processed sample, to the controller. Processing improves transmission reliability over an unreliable wireless channel, but incurs an additional cost. The objective is to minimize the long-term average age of information (AoI), subject to con…

Onboard Neuromorphic Split Computing via Optical Links for LEO Remote Sensing
Zihang Song, Petar Popovski
https://arxiv.org/abs/2507.08490 https://

Onboard Neuromorphic Split Computing via Optical Links for LEO Remote Sensing
Low Earth orbit (LEO) satellite constellations increasingly require onboard intelligence under strict power and communication constraints. This paper proposes a neuromorphic split computing framework tailored for hierarchical LEO systems, where edge satellites perform event-driven sensing using dynamic vision sensors (DVS) and lightweight spiking neural network (SNN) encoders, while core satellites conduct inference using a powerful SNN decoder. A learned spike mapping scheme enables direct tra…

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
Abhinav Sood, Maria Teresa Llano, Jon McCormack
https://arxiv.org/abs/2507.08260

Do Conversational Interfaces Limit Creativity? Exploring Visual Graph Systems for Creative Writing
We present a graphical, node-based system through which users can visually chain generative AI models for creative tasks. Research in the area of chaining LLMs has found that while chaining provides transparency, controllability and guardrails to approach certain tasks, chaining with pre-defined LLM steps prevents free exploration. Using cognitive processes from creativity research as a basis, we create a system that addresses the inherent constraints of chat-based AI interactions. Specifically…

Deep Finite Temperature Bootstrap
V. Niarchos, C. Papageorgakis, A. Stratoudakis, M. Woolley
https://arxiv.org/abs/2508.08560 https://arxiv.org/pdf/2508.08…

Deep Finite Temperature Bootstrap
We introduce a novel method to bootstrap crossing equations in Conformal Field Theory and apply it to finite temperature theories on $S^1\times \mathbb{R}^{d-1}$. The proposed approach does not rely on positivity constraints and does not employ uncontrolled truncation schemes. Instead, we capture the contribution of an infinite number of operators in conformal block expansions using suitable functions, which are bootstrapped (numerically) together with a finite number of exposed CFT data. Our a…

from my link log —
Let's take esoteric programming languages seriously.
https://arxiv.org/abs/2505.15327
saved 2025-10-11 https://dotat.at/:/XKTKR.…

Let's Take Esoteric Programming Languages Seriously
Esoteric programming languages are challenging to learn, but their unusual features and constraints may serve to improve programming ability. From languages designed to be intentionally obtuse (e.g. INTERCAL) to others targeting artistic expression (e.g. Piet) or exploring the nature of computation (e.g. Fractan), there is rich variety in the realm of esoteric programming languages. This essay examines the counterintuitive appeal of esoteric languages and seeks to analyse reasons for this popul…

Generating Query-Relevant Document Summaries via Reinforcement Learning
Nitin Yadav, Changsung Kang, Hongwei Shang, Ming Sun
https://arxiv.org/abs/2508.08404 https://

Generating Query-Relevant Document Summaries via Reinforcement Learning
E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predictions, as product titles often lack sufficient detail to capture query intent. While product descriptions provide richer information, their verbosity and length make them unsuitable for real-time ranking, particularly for computationally expensive architectures like cross-encoder ranking models. To address this challe…

Normative Moral Pluralism for AI: A Framework for Deliberation in Complex Moral Contexts
David-Doron Yaacov
https://arxiv.org/abs/2508.08333 https://arxiv.…

Normative Moral Pluralism for AI: A Framework for Deliberation in Complex Moral Contexts
The conceptual framework proposed in this paper centers on the development of a deliberative moral reasoning system - one designed to process complex moral situations by generating, filtering, and weighing normative arguments drawn from diverse ethical perspectives. While the framework is rooted in Machine Ethics, it also makes a substantive contribution to Value Alignment by outlining a system architecture that links structured moral reasoning to action under time constraints. Grounded in norm…

Generative AI-Enabled Robust 6G Uplink: Principles, Challenges, and Directions
Chunmei Xu, Yi Ma, Rahim Tafazolli, Peiying Zhu
https://arxiv.org/abs/2508.09348 https://

Generative AI-Enabled Robust 6G Uplink: Principles, Challenges, and Directions
Next-generation wireless networks (6G) face a critical uplink bottleneck due to stringent device-side resource constraints and challenging channel conditions. This article introduces GenCom, a novel system-level paradigm for robust 6G uplink that leverages Generative AI and exploits the inherent resource imbalance between transmitters and receivers. In GenCom, resource-rich receivers deploy powerful offline-trained GenAI models to reconstruct high semantic-fidelity content from degraded signals…

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
Samet Uzun, Behcet Acikmese, John M. Carson III
https://arxiv.org/abs/2510.09610

Sequential Convex Programming for 6-DoF Powered Descent Guidance with Continuous-Time Compound State-Triggered Constraints
This paper presents a sequential convex programming (SCP) framework for ensuring the continuous-time satisfaction of compound state-triggered constraints, a subset of logical specifications, in the powered descent guidance (PDG) problem. The proposed framework combines the generalized mean-based smooth robustness measure (D-GMSR), a parameterization technique tailored for expressing discrete-time temporal and logical specifications through smooth functions, with the continuous-time successive c…

Distribution-Free Prediction Sets for Regression under Target Shift
Menghan Yi, Yanlin Tang, Huixia Judy Wang
https://arxiv.org/abs/2510.10985 https://arxi…

Distribution-Free Prediction Sets for Regression under Target Shift
In real-world applications, the limited availability of labeled outcomes presents significant challenges for statistical inference due to high collection costs, technical barriers, and other constraints. In this work, we propose a method to construct efficient conformal prediction sets for new target outcomes by leveraging a source distribution that is distinct from the target but related through a distributional shift assumption and provides abundant labeled data. When the target data are full…

Sensitivity of $W$-boson measurements to low-mass right-handed neutrinos
Rodrigo Alonso, Sam Bates, Chris Hays, Chris Pollard, Michael Spannowsky
https://arxiv.org/abs/2508.08903

Sensitivity of $W$-boson measurements to low-mass right-handed neutrinos
A low-mass right-handed neutrino could interact with electroweak bosons via mixing, a mediator particle, or loop corrections. Using an effective field theory, we determine constraints on these interactions from $W$-boson measurements at hadron colliders. Due to the difference in the initial states at the Tevatron and the LHC, $W$-boson decays to a right-handed neutrino would artificially increase the mass measured at the Tevatron while only affecting the difference between $W^+$ and $W^-$ mass …

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
Kentaro Seki, Shinnosuke Takamichi, Takaaki Saeki, Hiroshi Saruwatari
https://arxiv.org/abs/2507.08319

Active Learning for Text-to-Speech Synthesis with Informative Sample Collection
The construction of high-quality datasets is a cornerstone of modern text-to-speech (TTS) systems. However, the increasing scale of available data poses significant challenges, including storage constraints. To address these issues, we propose a TTS corpus construction method based on active learning. Unlike traditional feed-forward and model-agnostic corpus construction approaches, our method iteratively alternates between data collection and model training, thereby focusing on acquiring data …

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic
https://arxiv.org/abs/2510.11011

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve higher accuracy for complex access patterns. However, these methods often struggle with today's dynamic, ever-growing datasets and require frequent, timely fine-tuning. Privacy constraints may also restrict a…

Age of Information-Aware Cognitive Shared Access Networks with Energy Harvesting
Georgios Smpokos, Dionysis Xenakis, Marios Kountouris, Nikolaos Pappas
https://arxiv.org/abs/2510.11198

Age of Information-Aware Cognitive Shared Access Networks with Energy Harvesting
This study investigates a cognitive shared access network with energy harvesting capabilities operating under Age of Information (AoI) constraints for the primary user. Secondary transmitters are spatially distributed according to a homogeneous Poisson Point Process (PPP), while the primary user is located at a fixed position. The primary transmitter handles bursty packet arrivals, whereas secondary users operate under saturated traffic conditions. To manage interference and energy, two distinc…

Parameterized Algorithms for Diversity of Networks with Ecological Dependencies
Mark Jones, Jannik Schestag
https://arxiv.org/abs/2510.09512 https://arxiv.…

Parameterized Algorithms for Diversity of Networks with Ecological Dependencies
For a phylogenetic tree, the phylogenetic diversity of a set A of taxa is the total weight of edges on paths to A. Finding small sets of maximal diversity is crucial for conservation planning, as it indicates where limited resources can be invested most efficiently. In recent years, efficient algorithms have been developed to find sets of taxa that maximize phylogenetic diversity either in a phylogenetic network or in a phylogenetic tree subject to ecological constraints, such as a food web. Ho…

Concept-Based Generic Programming in C
Bjarne Stroustrup
https://arxiv.org/abs/2510.08969 https://arxiv.org/pdf/2510.08969…

Concept-Based Generic Programming in C++
We present programming techniques to illustrate the facilities and principles of C++ generic programming using concepts. Concepts are C++'s way to express constraints on generic code. As an initial example, we provide a simple type system that eliminates narrowing conversions and provides range checking without unnecessary notational or run-time overheads. Concepts are used throughout to provide user-defined extensions to the type system. The aim is to show their utility and the fundamental ide…

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
Takehiro Ishikawa
https://arxiv.org/abs/2510.08591

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
Recent advancements in QML and SNNs have generated considerable excitement, promising exponential speedups and brain-like energy efficiency to revolutionize AI. However, this paper argues that they are unlikely to displace DNNs in the near term. QML struggles with adapting backpropagation due to unitary constraints, measurement-induced state collapse, barren plateaus, and high measurement overheads, exacerbated by the limitations of current noisy intermediate-scale quantum hardware, overfitting…

Semilinear spaces of matrices with transitivity, rank or spectral constraints
Cl\'ement de Seguins Pazzis
https://arxiv.org/abs/2508.06934 https://arxi…

Semilinear spaces of matrices with transitivity, rank or spectral constraints
Let $\mathbb{D}$ be a division ring with finite degree over a central subfield $\mathbb{F}$. Under mild cardinality assumptions on $\mathbb{F}$, we determine the greatest possible dimension for an $\mathbb{F}$-linear subspace of $M_n(\mathbb{D})$ in which no matrix has a nonzero fixed vector, and we elucidate the structure of the spaces that have the greatest dimension. The classification involves the associative composition algebras over $\mathbb{F}$ and requires the study of intransitive spac…

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
toXiv_bot_toot

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
N. Nazari Pooya
https://arxiv.org/abs/2510.08875 https://

Constraints on the interacting holographic dark energy models: implications from background and perturbations data
In this study, we employ a two-step method to analyze models of holographic dark energy (HDE) and interacting holographic dark energy (IHDE), incorporating three distinct dark energy (DE)-dark matter (DM) interaction terms. First, using the latest background dataset, we conduct a Markov chain Monte Carlo (MCMC) analysis to constrain the free parameters of the models. Then, we assess the models against each other using the key background parameters and compare them to the $Λ$CDM standard model.…

Joint Model-based Model-free Diffusion for Planning with Constraints
Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Yongxin Chen, Danfei Xu, Shreyas Kousik
https://arxiv.org/abs/2509.08775

Joint Model-based Model-free Diffusion for Planning with Constraints
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Naively integrating these modules presents compatibility challenges when diffusion's multi-modal outputs behave adversarially to optimization-based modules. To address this, we introduce Joint Model-based Model-free Diffusion (JM2D), a novel generative modeling framework. JM2D fo…

Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
Michele Minervini, Madison Chin, Jacob Kupperman, Nana Liu, Ivy Luo, Meghan Ly, Soorya Rethinasamy, Kathie Wang, Mark M. Wilde
https://arxiv.org/abs/2508.09103

Constrained free energy minimization for the design of thermal states and stabilizer thermodynamic systems
A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system subject to constraints on the charges. Recently, [Liu et al., arXiv:2505.04514] proposed first- and second-order classical and hybrid quantum-classical algorithms for solving a dual chemical potential maximization problem, and they proved that these algorithms converge to global optima by means of gradient-ascent app…

Spin Constraints on 4U 1630-47 via combined Continuum Fitting and Reflection methods: a comparative study using Frequentist and Bayesian statistics
Debtroy Das, Honghui Liu, Zuobin Zhang, Cosimo Bambi, Jiachen Jiang, Johannes Buchner, Andrea Santangelo, Menglei Zhou
https://arxiv.org/abs/2509.09481…

Spin Constraints on 4U 1630-47 via combined Continuum Fitting and Reflection methods: a comparative study using Frequentist and Bayesian statistics
We present a comprehensive Bayesian spectral analysis of the black hole X-ray binary 4U 1630-47 during its 2022 outburst, using simultaneous \textit{NICER} and \textit{NuSTAR} observations. Using the traditional frequentist approach, we build our model combining reflection spectroscopy with continuum fitting techniques and analyse the data. In the Bayesian framework, we jointly constrain the black hole's spin, mass, inclination, and distance within a unified framework. Employing nested sampling…

Generalized Quasi-Cyclic LDPC Codes: Design and Efficient Encoding
Roxana Smarandache, David G. M. Mitchell, Anthony G\'omez-Fonseca
https://arxiv.org/abs/2508.07030 https:/…

Generalized Quasi-Cyclic LDPC Codes: Design and Efficient Encoding
Generalized low-density parity-check (GLDPC) codes, where single parity-check constraints on the code bits are replaced with generalized constraints (an arbitrary linear code), are a promising class of codes for low-latency communication. The block error rate performance of the GLDPC codes, combined with a complementary outer code, has been shown to outperform a variety of state-of-the-art code and decoder designs with suitable lengths and rates for the 5G ultra-reliable low-latency communicati…

CoNeT-GIANT: A compressed Newton-type fully distributed optimization algorithm
Souvik Das, Subhrakanti Dey
https://arxiv.org/abs/2510.08806 https://arxiv.o…

CoNeT-GIANT: A compressed Newton-type fully distributed optimization algorithm
Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents a communication-efficient second-order distributed optimization algorithm, termed as CoNet-GIANT, equipped with a compression module, designed to minimize the average of local strongly convex functions. CoNet-GIANT incorporates two consensus-based averaging …

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Tian-Nuo Li, Guo-Hong Du, Yun-He Li, Yichao Li, Jia-Le Ling, Jing-Fei Zhang, Xin Zhang
https://arxiv.org/abs/2510.11363

Updated constraints on interacting dark energy: A comprehensive analysis using multiple CMB probes, DESI DR2, and supernovae observations
Recent DESI baryon acoustic oscillation (BAO) measurements, combined with Planck cosmic microwave background (CMB) data and DESY5 type Ia supernova (SN) data, indicate a significant deviation from $Λ$CDM, which seems to suggest that this deviation can be explained by an interaction between dark energy and dark matter. In this work, we perform a comprehensive analysis by utilizing the latest DESI DR2 BAO data in conjunction with CMB data from ACT, SPT, Planck, and WMAP, along with SN data from …

When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital
Avni Kothari, Patrick Vossler, Jean Digitale, Mohammad Forouzannia, Elise Rosenberg, Michele Lee, Jennee Bryant, Melanie Molina, James Marks, Lucas Zier, Jean Feng
https://arxiv.org/abs/2508.085…

When the Domain Expert Has No Time and the LLM Developer Has No Clinical Expertise: Real-World Lessons from LLM Co-Design in a Safety-Net Hospital
Large language models (LLMs) have the potential to address social and behavioral determinants of health by transforming labor intensive workflows in resource-constrained settings. Creating LLM-based applications that serve the needs of underserved communities requires a deep understanding of their local context, but it is often the case that neither LLMs nor their developers possess this local expertise, and the experts in these communities often face severe time/resource constraints. This crea…

A Compact Story of Positivity in de Sitter
Priyesh Chakraborty, Timothy Cohen, Daniel Green, Yiwen Huang
https://arxiv.org/abs/2508.08359 https://arxiv.org…

A Compact Story of Positivity in de Sitter
Recent developments have yielded significant progress towards systematically understanding loop corrections to de Sitter (dS) correlators. In close analogy with physics in Anti-de Sitter (AdS), large logarithms can result from loops that can be interpreted as corrections to the dimensions of operators. In contrast with AdS, these dimensions are not manifestly real. This implies that the theoretical constraints on the associated correlators are less transparent, particularly in the presence of l…

Bandits with Single-Peaked Preferences and Limited Resources
Gur Keinan, Rotem Torkan, Omer Ben-Porat
https://arxiv.org/abs/2510.09425 https://arxiv.org/pd…

Bandits with Single-Peaked Preferences and Limited Resources
We study an online stochastic matching problem in which an algorithm sequentially matches $U$ users to $K$ arms, aiming to maximize cumulative reward over $T$ rounds under budget constraints. Without structural assumptions, computing the optimal matching is NP-hard, making online learning computationally infeasible. To overcome this barrier, we focus on \emph{single-peaked preferences} -- a well-established structure in social choice theory, where users' preferences are unimodal with respect to…

Replaced article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
[1/1]:
- Population demographics of white dwarf binaries with intermediate separations: Gaia constraints o...
Natsuko Yamaguchi, Kareem El-Badry, Sahar Shahaf

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
Juan Jos\'e Burred, Carmine-Emanuele Cella
https://arxiv.org/abs/2510.08816 http…

Audible Networks: Deconstructing and Manipulating Sounds with Deep Non-Negative Autoencoders
We propose the use of Non-Negative Autoencoders (NAEs) for sound deconstruction and user-guided manipulation of sounds for creative purposes. NAEs offer a versatile and scalable extension of traditional Non-Negative Matrix Factorization (NMF)-based approaches for interpretable audio decomposition. By enforcing non-negativity constraints through projected gradient descent, we obtain decompositions where internal weights and activations can be directly interpreted as spectral shapes and temporal …

Topological Landscapes of the BSM Higgs Sector
Jyotiranjan Beuria
https://arxiv.org/abs/2510.10900 https://arxiv.org/pdf/2510.10900

Topological Landscapes of the BSM Higgs Sector
We explore the structure of the parameter space in the Singlet Scalar Dark Matter (SSDM) model and the Next-to-Two Higgs Doublet Model (N2HDM) with $\tanβ= 5$ and $\tanβ= 45$. Parameter points are classified as allowed or excluded based on compatibility with the Higgs observation constraints. Using a combined framework of Topological Data Analysis (TDA), Uniform Manifold Approximation and Projection (UMAP), and Linear Discriminant Analysis (LDA), we characterize the global geometry and topolo…

Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods
Kun Huang, Huiyan Sang
https://arxiv.org/abs/2508.08324 https://arxi…

Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods
Bayesian model-based spatial clustering methods are widely used for their flexibility in estimating latent clusters with an unknown number of clusters while accounting for spatial proximity. Many existing methods are designed for clustering finite spatial units, limiting their ability to make predictions, or may impose restrictive geometric constraints on the shapes of subregions. Furthermore, the posterior clustering consistency theory of spatial clustering models remains largely unexplored in…

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

Optical Phenomena in a Non-Commutative Kalb-Ramond Black Hole Spacetime
A. A. Ara\'ujo Filho, N. Heidari, Iarley P. Lobo, Yuxuan Shi
https://arxiv.org/abs/2508.08889 https:/…

Optical Phenomena in a Non-Commutative Kalb-Ramond Black Hole Spacetime
This work investigates additional gravitational features of a newly proposed black hole spacetime within Kalb-Ramond gravity, incorporating non-commutative corrections arising from a gauge-theoretic approach recently introduced in the literature [arXiv:2507.17390]. Accordingly, null geodesics are solved numerically to trace photon paths; the photon sphere and shadow are determined. From Event Horizon Telescope (EHT) measurements of $Sgr A^{*}$, constraints on the parameters $Θ$ (which encapsul…

Hybrid MAC Protocol with Integrated Multi-Layered Security for Resource-Constrained UAV Swarm Communications
Dhrumil Bhatt, Siddharth Penumatsa, Vidushi Kumar
https://arxiv.org/abs/2510.10236

Hybrid MAC Protocol with Integrated Multi-Layered Security for Resource-Constrained UAV Swarm Communications
Flying Ad Hoc Networks (FANETs) present unique challenges due to high node mobility, dynamic topologies, and strict resource constraints. Existing routing protocols often optimize for a single metric, such as path length or energy, while neglecting the complex dependencies between network performance, security, and MAC layer efficiency. This paper introduces a novel hardware software co design framework for secure and adaptive UAV swarm communications, featuring an energy aware protocol stack. …

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Gaofeng Li, Peisen Xu, Ruize Wang, Qi Ye, Jiming Chen, Dezhen Song, Yanlong Huang
https://arxiv.org/abs/2510.07986

Orientation Learning and Adaptation towards Simultaneous Incorporation of Multiple Local Constraints
Orientation learning plays a pivotal role in many tasks. However, the rotation group SO(3) is a Riemannian manifold. As a result, the distortion caused by non-Euclidean geometric nature introduces difficulties to the incorporation of local constraints, especially for the simultaneous incorporation of multiple local constraints. To address this issue, we propose the Angle-Axis Space-based orientation representation method to solve several orientation learning problems, including orientation adap…

Constraints on the varying electron mass and early dark energy in light of ACT DR6 and DESI DR2 and the implications for inflation
Yo Toda, Osamu Seto
https://arxiv.org/abs/2508.09025

Constraints on the varying electron mass and early dark energy in light of ACT DR6 and DESI DR2 and the implications for inflation
Primarily motivated by the Hubble tension, we analyze the varying electron mass model and axionlike early dark energy model (EDE) using baryon acoustic oscillation data from DESI DR2 data and including the recent results from ACT DR6. Our analysis indicates that $m_{e} / m_{e0} = 1.0081 \pm 0.0046 $ and the energy fraction of EDE is constrained as $f_\mathrm{EDE} < 0.016$. Since those cosmological models fit with different spectral index $n_s$, we show the posterior of those models on the ($n_s…

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
Thomas Michel, Debabrota Basu, Emilie Kaufmann
https://arxiv.org/abs/2508.06377 https://…

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
We revisit Wald's celebrated Sequential Probability Ratio Test for sequential tests of two simple hypotheses, under privacy constraints. We propose DP-SPRT, a wrapper that can be calibrated to achieve desired error probabilities and privacy constraints, addressing a significant gap in previous work. DP-SPRT relies on a private mechanism that processes a sequence of queries and stops after privately determining when the query results fall outside a predefined interval. This OutsideInterval mecha…

Quantum Algorithms for Projection-Free Sparse Convex Optimization
Jianhao He, John C. S. Lui
https://arxiv.org/abs/2507.08543 https://

Quantum Algorithms for Projection-Free Sparse Convex Optimization
This paper considers the projection-free sparse convex optimization problem for the vector domain and the matrix domain, which covers a large number of important applications in machine learning and data science. For the vector domain $\mathcal{D} \subset \mathbb{R}^d$, we propose two quantum algorithms for sparse constraints that finds a $\varepsilon$-optimal solution with the query complexity of $O(\sqrt{d}/\varepsilon)$ and $O(1/\varepsilon)$ by using the function value oracle, reducing a fa…

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

Distributionally Robust Control with Constraints on Linear Unidimensional Projections
Alexandros E. Tzikas, Lukas Fiechtner, Arec Jamgochian, Mykel J. Kochenderfer
https://arxiv.org/abs/2508.07121

Distributionally Robust Control with Constraints on Linear Unidimensional Projections
Distributionally robust control is a well-studied framework for optimal decision making under uncertainty, with the objective of minimizing an expected cost function over control actions, assuming the most adverse probability distribution from an ambiguity set. We consider an interpretable and expressive class of ambiguity sets defined by constraints on the expected value of functions of one-dimensional linear projections of the uncertain parameters. Prior work has shown that, under conditions,…

Locally Optimal Private Sampling: Beyond the Global Minimax
Hrad Ghoukasian, Bonwoo Lee, Shahab Asoodeh
https://arxiv.org/abs/2510.09485 https://arxiv.org/…

Locally Optimal Private Sampling: Beyond the Global Minimax
We study the problem of sampling from a distribution under local differential privacy (LDP). Given a private distribution $P \in \mathcal{P}$, the goal is to generate a single sample from a distribution that remains close to $P$ in $f$-divergence while satisfying the constraints of LDP. This task captures the fundamental challenge of producing realistic-looking data under strong privacy guarantees. While prior work by Park et al. (NeurIPS'24) focuses on global minimax-optimality across a class …

Projected Gradient Descent for Constrained Decision-Dependent Optimization
Zifan Wang, Changxin Liu, Thomas Parisini, Michael M. Zavlanos, Karl H. Johansson
https://arxiv.org/abs/2508.08856

Projected Gradient Descent for Constrained Decision-Dependent Optimization
This paper considers the decision-dependent optimization problem, where the data distributions react in response to decisions affecting both the objective function and linear constraints. We propose a new method termed repeated projected gradient descent (RPGD), which iteratively projects points onto evolving feasible sets throughout the optimization process. To analyze the impact of varying projection sets, we show a Lipschitz continuity property of projections onto varying sets with an explic…

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle \sigma v \rangle$ after 11 years of observations
E. I. Zakharov, V. V. Barinov, D. S. Gorbunov, R. A. Krivonos, A. A. Mukhin
https://arxiv.org/abs/2509.08506

Search for a photon peak from keV-scale dark matter annihilation with NuSTAR: Constraints on $\langle σv \rangle$ after 11 years of observations
We report new constraints on the velocity-independent annihilation cross section $\langle σv \rangle$ of keV-scale dark matter particles based on 11 years of observations with the NuSTAR X-ray telescope. Using the unfocused stray light mode of the instrument, which provides a wide field-of-view and a stable instrumental background, we perform a sensitive search for photon signatures from dark matter annihilation in the Galactic halo. We model the resulting diffuse X-ray spectrum over the 3-20 …

Addressing the $R_{\tau/{\mu,e}}\left(D^{(*)}\right)$ puzzle through New Physics four-fermion operators and their impact on $\Lambda_{b}\rightarrow\Lambda_{c}\tau\bar{\nu}_{\tau}$ decay
Muhammad Arslan, Ishtiaq Ahmed, Muhammad Jamil Aslam, Saba Shafaq, Tahira Yasmeen
https://arxiv.org/abs/2510.11564

Addressing the $R_{τ/{μ,e}}\left(D^{(*)}\right)$ puzzle through New Physics four-fermion operators and their impact on $Λ_{b}\rightarrowΛ_{c}τ\barν_τ$ decay
The Lepton Flavor Universality ratio $R_{τ/{μ,e}}\left(D^{(*)}\right)$ poses a challenge to the Standard Model (SM), as B-factory experiments, BaBar, Belle, and the LHCb show $3.31σ$ deviations from their theoretical predictions. Utilizing the latest HFLAV averages and incorporating the branching ratio constraints $60\%$, $30\%$ and $10\%$ from the lifetime of the $B_c$ meson, we determine the values of the Wilson coefficients (WCs) for different New Physics (NP) four-fermion operator with s…

Koopman Operator Based Linear Model Predictive Control for Quadruped Trotting
Chun-Ming Yang, Pranav A. Bhounsule
https://arxiv.org/abs/2508.08259 https://…

Koopman Operator Based Linear Model Predictive Control for Quadruped Trotting
Online optimal control of quadruped robots would enable them to adapt to varying inputs and changing conditions in real time. A common way of achieving this is linear model predictive control (LMPC), where a quadratic programming (QP) problem is formulated over a finite horizon with a quadratic cost and linear constraints obtained by linearizing the equations of motion and solved on the fly. However, the model linearization may lead to model inaccuracies. In this paper, we use the Koopman opera…

Universal syndrome-based recovery for noise-adapted quantum error correction
Debjyoti Biswas, Prabha Mandayam
https://arxiv.org/abs/2510.08719 https://arxi…

Universal syndrome-based recovery for noise-adapted quantum error correction
Quantum error correction (QEC) is an essential tool for quantum computing that enables reliable information processing in the presence of noise. Syndrome measurements play a central role in QEC, making it possible to unambiguously identify the location and type of errors. While syndrome extraction is natural for conventional QEC protocols, where the errors satisfy certain algebraic constraints \emph{perfectly}, this feature is largely missing in the framework of approximate or noise-adapted QEC…

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Jyotirmoy V. Deshmukh, Lars Lindemann, Bistra Dilkina
https://arxiv.org/abs/2508.07515

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Autonomous systems must solve motion planning problems subject to increasingly complex, time-sensitive, and uncertain missions. These problems often involve high-level task specifications, such as temporal logic or chance constraints, which require solving large-scale Mixed-Integer Linear Programs (MILPs). However, existing MILP-based planning methods suffer from high computational cost and limited scalability, hindering their real-time applicability. We propose to use a neuro-symbolic approach…

The Law-Following AI Framework: Legal Foundations and Technical Constraints. Legal Analogues for AI Actorship and technical feasibility of Law Alignment
Katalina Hernandez Delgado
https://arxiv.org/abs/2509.08009

The Law-Following AI Framework: Legal Foundations and Technical Constraints. Legal Analogues for AI Actorship and technical feasibility of Law Alignment
This paper critically evaluates the "Law-Following AI" (LFAI) framework proposed by O'Keefe et al. (2025), which seeks to embed legal compliance as a superordinate design objective for advanced AI agents and enable them to bear legal duties without acquiring the full rights of legal persons. Through comparative legal analysis, we identify current constructs of legal actors without full personhood, showing that the necessary infrastructure already exists. We then interrogate the framework's clai…

Experimental Validation of Provably Covert Communication Using Software-Defined Radio
Rohan Bali, Trevor E. Bailey, Michael S. Bullock, Boulat A. Bash
https://arxiv.org/abs/2508.08380

Experimental Validation of Provably Covert Communication Using Software-Defined Radio
The fundamental information-theoretic limits of covert, or low probability of detection/intercept (LPD/LPI), communication have been extensively studied for over a decade, resulting in the square root law (SRL): only $L\sqrt{n}$ covert bits can be reliably transmitted over time-bandwidth product $n$, for constant $L>0$. Transmitting more either results in detection or decoding errors. The SRL imposes significant constraints on hardware realization of mathematically-guaranteed covert communicati…

Constraints from the Giant Arc in Abell 370. A New Framework for Understanding Systematic Errors in Cluster Lens Modeling. IV. Constraints from the Giant Arc in Abell 370
Lana Eid, Charles R. Keeton
https://arxiv.org/abs/2509.08227

Constraints from the Giant Arc in Abell 370. A New Framework for Understanding Systematic Errors in Cluster Lens Modeling. IV. Constraints from the Giant Arc in Abell 370
We aim to improve cluster lens modeling and source reconstruction by utilizing the full information in giant, caustic-crossing arcs lensed by galaxy clusters. Lens models are generally constrained using image positions and assuming point sources, but spatially extended giant arcs provide more constraints; however, they require a more complex model that accounts for the structure of the extended source. We seek to determine whether improvements to the lens model and reconstructed source merit th…

What Are We Clustering For? Establishing Performance Guarantees for Time Series Aggregation in Generation Expansion Planning
Luca Santosuosso, Bettina Klinz, Sonja Wogrin
https://arxiv.org/abs/2510.09357

What Are We Clustering For? Establishing Performance Guarantees for Time Series Aggregation in Generation Expansion Planning
Generation expansion planning (GEP) is a prominent example of capacity expansion problems in operations research. Being generally NP-hard, GEP optimization models can become intractable when nonconvex dynamics, time-coupling constraints, and complex asset interactions are involved. Time series aggregation (TSA) tackles this by reducing temporal complexity via input data clustering. However, existing TSA methods either focus solely on preserving the statistical features of the input data, yieldi…

Reactive Model Predictive Contouring Control for Robot Manipulators
Junheon Yoon, Woo-Jeong Baek, Jaeheung Park
https://arxiv.org/abs/2508.09502 https://ar…

Reactive Model Predictive Contouring Control for Robot Manipulators
This contribution presents a robot path-following framework via Reactive Model Predictive Contouring Control (RMPCC) that successfully avoids obstacles, singularities and self-collisions in dynamic environments at 100 Hz. Many path-following methods rely on the time parametrization, but struggle to handle collision and singularity avoidance while adhering kinematic limits or other constraints. Specifically, the error between the desired path and the actual position can become large when executi…

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Pau Escofet, Abhijit Das, Sahar Ben Rached, Santiago Rodrigo, Jordi Domingo, Fabio Sebastiano, Masoud Babaie, Batuhan Keskin, Edoardo Charbon, Peter Haring Bol\'ivar, Maurizio Palesi, Elena Blokhina, Bogdan Staszewski, Avishek Nag, Artur Garcia-S\'aez, Sergi Abadal, Eduard Alarc\'on, Carmen G. Almud\'ever

On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
Modular architectures are a promising approach to scaling quantum computers beyond the limits of monolithic designs. However, non-local communications between different quantum processors might significantly impact overall system performance. In this work, we investigate the role of the network infrastructure in modular quantum computing architectures, focusing on coherence loss due to communication constraints. We analyze the impact of classical network latency on quantum teleportation and ide…

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Tsuyoshi Okita
https://arxiv.org/abs/2510.08295

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
Conventional time-series generation often ignores domain-specific physical constraints, limiting statistical and physical consistency. We propose a hierarchical framework that embeds the inherent hierarchy of physical laws-conservation, dynamics, boundary, and empirical relations-directly into deep generative models, introducing a new paradigm of physics-informed inductive bias. Our method combines Fourier Neural Operators (FNOs) for learning physical operators with Conditional Flow Matching (C…

Dark matter production from evaporation of regular primordial black holes
Ngo Phuc Duc Loc
https://arxiv.org/abs/2510.09395 https://arxiv.org/pdf/2510.0939…

Dark matter production from evaporation of regular primordial black holes
We study dark matter (DM) production from evaporation of regular primordial black holes (RPBHs). While there are many available metrics that describe singularity-free RPBHs, we focus on the Hayward metric and the Simpson-Visser metric for illustrative purpose. The framework can be applied straightforwardly to other metrics. RPBHs generally have different Hawking temperature and therefore different lifetime from the standard singular black holes. The resulting modified cosmological constraints f…

Coherence of Supermassive Black Hole Binary Demographics with the nHz Stochastic Gravitational Wave Background
Katsunori Kusakabe, Yoshiyuki Inoue, Daisuke Toyouchi
https://arxiv.org/abs/2510.10548

Coherence of Supermassive Black Hole Binary Demographics with the nHz Stochastic Gravitational Wave Background
We present a refined estimation of the stochastic gravitational wave background (SGWB) based on observed dual active galactic nuclei (AGNs) together with AGN X-ray luminosity functions, in light of recent Pulsar Timing Array detections of an nHz SGWB. We identify a characteristic luminosity dependence in dual AGN fractions by compiling recent observational datasets, providing crucial constraints on supermassive black hole binary (SMBHB) populations. Our AGN-based model reproduces the current SG…

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
Noam Goldberg, Michael Poss, Shimrit Shtern
https://arxiv.org/abs/2510.08843 https://

Smooth Uncertainty Sets: Dependence of Uncertain Parameters via a Simple Polyhedral Set
We propose a novel polyhedral uncertainty set for robust optimization, termed the smooth uncertainty set, which captures dependencies of uncertain parameters by constraining their pairwise differences. The bounds on these differences may be dictated by the underlying physics of the problem and may be expressed by domain experts. When correlations are available, the bounds can be set to ensure that the associated probabilistic constraints are satisfied for any given probability. We explore spe…

Constraining Super-Heavy Dark Matter with the KM3-230213A Neutrino Event
Roberto Aloisio, Antonio Ambrosone, Carmelo Evoli
https://arxiv.org/abs/2508.08779 https://

Constraining Super-Heavy Dark Matter with the KM3-230213A Neutrino Event
Recently, the KM3NeT collaboration detected an astrophysical neutrino event, KM3-230213A, with an energy of approximately $220~\rm PeV$, providing unprecedented insights into the ultra-high-energy Universe. In this study, we introduce a novel likelihood framework designed to leverage this event to constrain the properties of super-heavy dark matter (SHDM) decay. Our approach systematically integrates multi-messenger constraints from galactic and extragalactic neutrino flux measurements by IceCu…

Phase-Coordinated Multi-Agent Circular Formation Control with Non-Concentric Boundary Constraints
Shubham Singh, Anoop Jain
https://arxiv.org/abs/2509.08534 https://

Phase-Coordinated Multi-Agent Circular Formation Control with Non-Concentric Boundary Constraints
This paper addresses the problem of collective circular motion control for unicycle agents, with the objective of achieving phase coordination of their velocity vectors while ensuring that their trajectories remain confined within a prescribed non-concentric circular boundary. To accommodate such nonuniform motion constraints, we build upon our earlier work and extend the use of Mobius transformation to a multi-agent framework. The Mobius transformation maps two nonconcentric circles to concent…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
https://arxiv.org/abs/2510.07266 https://a…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to generate a sequence of predictions which are broadcast to a collection of downstream decision makers. Each decision maker has their own utility function, as well as a vector of constraint functions, each mapping their actions and an adversarially selected state to reward or constraint violation terms. The downstream dec…

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
https://arxiv.org/abs/2510.07700

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We show that constraints on Model-Based Diffusion can lead to catastrophic performance degradation, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantl…

Brans-Dicke-like field for co-varying $G$ and $c$: observational constraints
J. Bezerra-Sobrinho, R. R. Cuzinatto, L. G. Medeiros, P. J. Pompeia
https://arxiv.org/abs/2509.09211

Brans-Dicke-like field for co-varying $G$ and $c$: observational constraints
Ref. [Symmetry 15 (2023) 709] introduced a Brans-Dicke-like framework wherein the scalar field $ϕ$ is composed of both $G$ and $c$ which, for this reason, co-vary according to $c^{3}/G=\text{constant}$. In this paper, we use observational data to constrain the supposed co-varying $G$ and $c$. The datasets include SN Ia, BAO and the value of $θ$ extracted from CMB data. A proxy function is demanded for the varying $c$ since the framework does not provide a closed set of equations for computing…

Designing Control Barrier Functions Using a Dynamic Backup Policy
Victor Freire, Marco M. Nicotra
https://arxiv.org/abs/2510.09810 https://arxiv.org/pdf/25…

Designing Control Barrier Functions Using a Dynamic Backup Policy
This paper presents a systematic approach to construct control barrier functions for nonlinear control affine systems subject to arbitrary state and input constraints. Taking inspiration from the reference governor literature, the proposed method defines a family of backup policies, parametrized by the equilibrium manifold of the system. The control barrier function is defined on the augmented state-and-reference space: given a state-reference pair, the approach quantifies the distance to const…

Inertial accelerated primal-dual algorithms for non-smooth convex optimization problems with linear equality constraints
Huan Zhang, Xiangkai Sun, Shengjie Li, Kok Lay Teo
https://arxiv.org/abs/2509.07306

Inertial accelerated primal-dual algorithms for non-smooth convex optimization problems with linear equality constraints
This paper is devoted to the study of an inertial accelerated primal-dual algorithm, which is based on a second-order differential system with time scaling, for solving a non-smooth convex optimization problem with linear equality constraints. We first introduce a second-order differential system with time scaling associated with the non-smooth convex optimization problem, and then obtain fast convergence rates for the primal-dual gap, the feasibility violation, and the objective residual along…

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Zezeng Li, Rui Yang, Ruochen Chen, ZhongXuan Luo, Liming Chen
https://arxiv.org/abs/2508.06266

ADPro: a Test-time Adaptive Diffusion Policy for Robot Manipulation via Manifold and Initial Noise Constraints
Diffusion policies have recently emerged as a powerful class of visuomotor controllers for robot manipulation, offering stable training and expressive multi-modal action modeling. However, existing approaches typically treat action generation as an unconstrained denoising process, ignoring valuable a priori knowledge about geometry and control structure. In this work, we propose the Adaptive Diffusion Policy (ADP), a test-time adaptation method that introduces two key inductive biases into the …

Interacting k-essence field with non-pressureless Dark Matter: Cosmological Dynamics and Observational Constraints
Saddam Hussain, Qiang Wu, Tao Zhu
https://arxiv.org/abs/2509.09202

Interacting k-essence field with non-pressureless Dark Matter: Cosmological Dynamics and Observational Constraints
We investigate a class of interacting dark energy and dark matter (DM) models, where dark energy is modeled as a $k$-essence scalar field with an inverse-square potential. Two general forms of interaction are considered: one proportional to the Hubble parameter, and another independent of the Hubble parameter, depending instead on combinations of the energy densities and pressures of the dark sectors. The dynamics are analyzed using a dynamical system stability framework by constructing an auto…

Besondere Anforderungen des automatisierten Fahrens an den Entwurf
Robert Graubohm, Markus Maurer
https://arxiv.org/abs/2508.09731 https://arxiv.org/pdf/25…

Besondere Anforderungen des automatisierten Fahrens an den Entwurf
The development of automated vehicles and automated driving functions is an exceptionally complex task that requires the integration of numerous, sometimes conflicting interests and various constraints already in the early stages of system design. This chapter explains important challenges in concept specifications for automated driving and presents a systematic process model that contributes to overcoming the special requirements in this field. In addition, it describes the successful implemen…

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 5. Extensions beyond the standard modelling of theoretical probes and systematic effects
Collaboration, Goh, Nouri-Zonoz, Pamuk, Ballardini, Bose, Ca\~nas-Herrera, Casas, Franco-Abell\'an, Ili\'c, Keil, Kunz, Le Brun, Lepori, Martinelli, Sakr, Sorrenti, Teixeira, Tutusaus, Blot, Bonici, Bonvin, Camera, Cardone, Carrilho, Di Domizio, Durrer, Farrens, Beauchamps, Joudaki, Moretti, Pezzotta, S\'anchez, …

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 5. Extensions beyond the standard modelling of theoretical probes and systematic effects
Euclid is expected to establish new state-of-the-art constraints on extensions beyond the standard LCDM cosmological model by measuring the positions and shapes of billions of galaxies. Specifically, its goal is to shed light on the nature of dark matter and dark energy. Achieving this requires developing and validating advanced statistical tools and theoretical prediction software capable of testing extensions of the LCDM model. In this work, we describe how the Euclid likelihood pipeline, Cos…

Optimal monophasic, asymmetric electric field pulses for selective transcranial magnetic stimulation (TMS) with minimised power and coil heating
Ke Ma, Andrey Vlasov, Zeynep B. Simsek, Jinshui Zhang, Yiru Li, Boshuo Wang, David L. K. Murphy, Jessica Y. Choi, Maya E. Clinton, Noreen Bukhari-Parlakturk, Angel V. Peterchev, Stephan M. Goetz
https://

Optimal monophasic, asymmetric electric field pulses for selective transcranial magnetic stimulation (TMS) with minimised power and coil heating
Transcranial magnetic stimulation (TMS) with asymmetric electric field pulses, such as monophasic, offers directional selectivity for neural activation but requires excessive energy. Previous pulse shape optimisation has been limited to symmetric pulses or heavily constrained variations of conventional waveforms without achieving general optimality in energy efficiency or neural selectivity. We implemented an optimisation framework that incorporates neuron model activation constraints and flexi…

FLAMINGO: Baryonic effects on the weak lensing scattering transform
Mariia Marinichenko, Marcel P. van Daalen, Elena Sellentin, Jeger C. Broxterman, Matthieu Schaller, Joop Schaye
https://arxiv.org/abs/2510.09761

FLAMINGO: Baryonic effects on the weak lensing scattering transform
The scattering transform is a wavelet-based statistic capable of capturing non-Gaussian features in weak lensing (WL) convergence maps and has been proven to tighten cosmological parameter constraints by accessing information beyond two-point functions. However, its application in cosmological inference requires a clear understanding of its sensitivity to astrophysical systematics, the most significant of which are baryonic effects. These processes substantially modify the matter distribution o…

Integrated Learning and Optimization to Control Load Demand and Wind Generation for Minimizing Ramping Cost in Real-Time Electricity Market
Imran Pervez, Omar Knio
https://arxiv.org/abs/2508.09774

Integrated Learning and Optimization to Control Load Demand and Wind Generation for Minimizing Ramping Cost in Real-Time Electricity Market
We developed a new integrated learning and optimization (ILO) methodology to predict context-aware unknown parameters in economic dispatch (ED), a crucial problem in power systems solved to generate optimal power dispatching decisions to serve consumer load. The ED formulation in the current study consists of load and renewable generation as unknown parameters in its constraints predicted using contextual information (e.g., prior load, temperature). The ILO framework train a neural network (NN)…

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 6: Impact of systematic uncertainties on the cosmological analysis
Collaboration, Blot, Tanidis, Ca\~nas-Herrera, Carrilho, Bonici, Camera, Cardone, Casas, Davini, Di Domizio, Farrens, Goh, Beauchamps, Ili\'c, Joudaki, Keil, Le Brun, Martinelli, Moretti, Pettorino, Pezzotta, Sakr, S\'anchez, Sciotti, Tutusaus, Ajani, Crocce, Fumagalli, Giocoli, Legrand, Lembo, Lesci, Girones, Nouri-Zonoz, Pamuk, Pourtsid…

Euclid preparation. Cosmology Likelihood for Observables in Euclid (CLOE). 6: Impact of systematic uncertainties on the cosmological analysis
Extracting cosmological information from the Euclid galaxy survey will require modelling numerous systematic effects during the inference process. This implies varying a large number of nuisance parameters, which have to be marginalised over before reporting the constraints on the cosmological parameters. This is a delicate process, especially with such a large parameter space, which could result in biased cosmological results. In this work, we study the impact of different choices for modellin…

Investigating the $w$CDM Model with Latest DESI BAO Observations
Manisg Yadav, Archana Dixit, M. S. Barak, Anirudh Pradhan
https://arxiv.org/abs/2510.09074 https://

Investigating the $w$CDM Model with Latest DESI BAO Observations
In this study, we explore the impact of various combinations of CMB-independent datasets, including the recent DESI BAO measurements, on the equation of state (EoS) of dark energy and other cosmological parameters within the framework of the dynamical dark energy model ($w$CDM). Assuming a constant EoS parameter for dark energy, we derive constraints on the free parameters of the model using observational datasets such as DESI BAO, BBN, Observational Hubble Data (OHD), and Pantheon Plus (SN$^+$…

Extending CSST Emulator to post-DESI era
Zhao Chen, Yu Yu
https://arxiv.org/abs/2510.09503 https://arxiv.org/pdf/2510.09503

Extending CSST Emulator to post-DESI era
The recent DESI BAO measurements have revealed a potential deviation from a cosmological constant, suggesting a dynamic nature of dark energy. To rigorously test this result, complementary probes such as weak gravitational lensing are crucial, demanding highly accurate and efficient predictions of the nonlinear matter power spectrum within the $w_0w_a$CDM framework. However, most existing emulators fail to cover the full parameter posterior from DESI DR2+CMB constraints in the $w_0\mbox{-}w_a$ …

The choice of Planck CMB likelihood in cosmological analyses
Hidde Jense, Marc Vi\~na, Erminia Calabrese, Colin Hill
https://arxiv.org/abs/2510.09430 https://

The choice of Planck CMB likelihood in cosmological analyses
We compare cosmological parameters from different Planck sky maps and likelihood pipelines, assessing robustness of cosmological results with respect to the choice of the latest Planck maps-likelihood combination. We show that, for the Planck multipole range retained in combination with ground-based observations, different products give very similar cosmological solutions; small remaining differences are reduced by the addition of other CMB datasets to Planck. In particular, constraints on exte…